Sparse Pseudospectral Approximation Method

Multivariate global polynomial approximations - such as polynomial chaos or stochastic collocation methods - are now in widespread use for sensitivity analysis and uncertainty quantification. The pseudospectral variety of these methods uses a numerical integration rule to approximate the Fourier-type coefficients of a truncated expansion in orthogonal polynomials. For problems in more than two or three dimensions, a sparse grid numerical integration rule offers accuracy with a smaller node set compared to tensor product approximation. However, when using a sparse rule to approximately integrate these coefficients, one often finds unacceptable errors in the coefficients associated with higher degree polynomials. By reexamining Smolyak's algorithm and exploiting the connections between interpolation and projection in tensor product spaces, we construct a sparse pseudospectral approximation method that accurately reproduces the coefficients of basis functions that naturally correspond to the sparse grid integration rule. The compelling numerical results show that this is the proper way to use sparse grid integration rules for pseudospectral approximation

Consensus recommendations for a standardized brain tumor imaging protocol for clinical trials in brain metastases.

A recent meeting was held on March 22, 2019, among the FDA, clinical scientists, pharmaceutical and biotech companies, clinical trials cooperative groups, and patient advocacy groups to discuss challenges and potential solutions for increasing development of therapeutics for central nervous system metastases. A key issue identified at this meeting was the need for consistent tumor measurement for reliable tumor response assessment, including the first step of standardized image acquisition with an MRI protocol that could be implemented in multicenter studies aimed at testing new therapeutics. This document builds upon previous consensus recommendations for a standardized brain tumor imaging protocol (BTIP) in high-grade gliomas and defines a protocol for brain metastases (BTIP-BM) that addresses unique challenges associated with assessment of CNS metastases. The "minimum standard" recommended pulse sequences include: (i) parameter matched pre- and post-contrast inversion recovery (IR)-prepared, isotropic 3D T1-weighted gradient echo (IR-GRE); (ii) axial 2D T2-weighted turbo spin echo acquired after injection of gadolinium-based contrast agent and before post-contrast 3D T1-weighted images; (iii) axial 2D or 3D T2-weighted fluid attenuated inversion recovery; (iv) axial 2D, 3-directional diffusion-weighted images; and (v) post-contrast 2D T1-weighted spin echo images for increased lesion conspicuity. Recommended sequence parameters are provided for both 1.5T and 3T MR systems. An "ideal" protocol is also provided, which replaces IR-GRE with 3D TSE T1-weighted imaging pre- and post-gadolinium, and is best performed at 3T, for which dynamic susceptibility contrast perfusion is included. Recommended perfusion parameters are given

Democratic population decisions result in robust policy-gradient learning: A parametric study with GPU simulations

High performance computing on the Graphics Processing Unit (GPU) is an emerging field driven by the promise of high computational power at a low cost. However, GPU programming is a non-trivial task and moreover architectural limitations raise the question of whether investing effort in this direction may be worthwhile. In this work, we use GPU programming to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and investigate its ability to learn a simplified navigation task using a policy-gradient learning rule stemming from Reinforcement Learning. The purpose of this paper is twofold. First, we want to support the use of GPUs in the field of Computational Neuroscience. Second, using GPU computing power, we investigate the conditions under which the said architecture and learning rule demonstrate best performance. Our work indicates that networks featuring strong Mexican-Hat-shaped recurrent connections in the top layer, where decision making is governed by the formation of a stable activity bump in the neural population (a "non-democratic" mechanism), achieve mediocre learning results at best. In absence of recurrent connections, where all neurons "vote" independently ("democratic") for a decision via population vector readout, the task is generally learned better and more robustly. Our study would have been extremely difficult on a desktop computer without the use of GPU programming. We present the routines developed for this purpose and show that a speed improvement of 5x up to 42x is provided versus optimised Python code. The higher speed is achieved when we exploit the parallelism of the GPU in the search of learning parameters. This suggests that efficient GPU programming can significantly reduce the time needed for simulating networks of spiking neurons, particularly when multiple parameter configurations are investigated. © 2011 Richmond et al

Effects of Sorafenib on Intra-Tumoral Interstitial Fluid Pressure and Circulating Biomarkers in Patients with Refractory Sarcomas (NCI Protocol 6948)

Purpose: Jain Sorafenib is a multi-targeted tyrosine kinase inhibitor with therapeutic efficacy in several malignancies. Sorafenib may exert its anti-neoplastic effect in part by altering vascular permeability and reducing intra-tumoral interstitial hypertension. As correlative science with a phase II study in patients with advanced soft-tissue sarcomas (STS), we evaluated the impact of this agent on intra-tumor interstitial fluid pressure (IFP), serum circulating biomarkers, and vascular density. Patients and Methods: Patients with advanced STS with measurable disease and at least one superficial lesion amenable to biopsy received sorafenib 400 mg twice daily. Intratumoral IFP and plasma and circulating cell biomarkers were measured before and after 1–2 months of sorafenib administration. Results were analyzed in the context of the primary clinical endpoint of time-to-progression (TTP). Results: In 15 patients accrued, the median TTP was 45 days (range 14–228). Intra-tumoral IFP measurements obtained in 6 patients at baseline showed a direct correlation with tumor size. Two patients with stable disease at two months had post-sorafenib IFP evaluations and demonstrated a decline in IFP and vascular density. Sorafenib significantly increased plasma VEGF, PlGF, and SDF1$\alpha$ and decreased sVEGFR-2 levels. Increased plasma SDF1$\alpha$ and decreased sVEGFR-2 levels on day 28 correlated with disease progression. Conclusions: Pretreatment intra-tumoral IFP correlated with tumor size and decreased in two evaluable patients with SD on sorafenib. Sorafenib also induced changes in circulating biomarkers consistent with expected VEGF pathway blockade, despite the lack of more striking clinical activity in this small series

The effects of improving sleep on mental health (OASIS): a randomised controlled trial with mediation analysis

BACKGROUND: Sleep difficulties might be a contributory causal factor in the occurrence of mental health problems. If this is true, improving sleep should benefit psychological health. We aimed to determine whether treating insomnia leads to a reduction in paranoia and hallucinations. METHODS: We did this single-blind, randomised controlled trial (OASIS) at 26 UK universities. University students with insomnia were randomly assigned (1:1) with simple randomisation to receive digital cognitive behavioural therapy (CBT) for insomnia or usual care, and the research team were masked to the treatment. Online assessments took place at weeks 0, 3, 10 (end of therapy), and 22. The primary outcome measures were for insomnia, paranoia, and hallucinatory experiences. We did intention-to-treat analyses. The trial is registered with the ISRCTN registry, number ISRCTN61272251. FINDINGS: Between March 5, 2015, and Feb 17, 2016, we randomly assigned 3755 participants to receive digital CBT for insomnia (n=1891) or usual practice (n=1864). Compared with usual practice, the sleep intervention at 10 weeks reduced insomnia (adjusted difference 4·78, 95% CI 4·29 to 5·26, Cohen's d=1·11; p<0·0001), paranoia (-2·22, -2·98 to -1·45, Cohen's d=0·19; p<0·0001), and hallucinations (-1·58, -1·98 to -1·18, Cohen's d=0·24; p<0·0001). Insomnia was a mediator of change in paranoia and hallucinations. No adverse events were reported. INTERPRETATION: To our knowledge, this is the largest randomised controlled trial of a psychological intervention for a mental health problem. It provides strong evidence that insomnia is a causal factor in the occurrence of psychotic experiences and other mental health problems. Whether the results generalise beyond a student population requires testing. The treatment of disrupted sleep might require a higher priority in mental health provision. FUNDING: Wellcome Trust